Inversion of TES and MOPITT Data

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Atmos. Chem. Phys. Discuss., 7, 17625–17662, 2007 Atmospheric www.atmos-chem-phys-discuss.net/7/17625/2007/ Chemistry ACPD © Author(s) 2007. This work is licensed and Physics 7, 17625–17662, 2007 under a Creative Commons License. Discussions

Inversion of TES and MOPITT data

D. B. A. Jones et al. Inversion analysis of carbon monoxide emissions using data from the TES and Title Page MOPITT satellite instruments Abstract Introduction Conclusions References D. B. A. Jones1, K. W. Bowman2, J. A. Logan3, C. L. Heald4, J. Liu1, M. Luo2, Tables Figures J. Worden2, and J. Drummond1

1 Department of Physics, University of Toronto, Toronto, Ontario, Canada J I 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA 3School of Engineering and Applied Sciences, Harvard University, Cambridge, J I Massachusetts, USA Back Close 4Center for Atmospheric Sciences, University of California, Berkeley, California, USA

Received: 9 October 2007 – Accepted: 19 November 2007 – Published: 5 December 2007 Full Screen / Esc Correspondence to: D. B. A. Jones ([email protected]) Printer-friendly Version

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17625 Abstract ACPD We conduct an inverse modeling analysis of measurements of atmospheric CO from the TES and MOPITT satellite instruments using the GEOS-Chem global chemical 7, 17625–17662, 2007 transport model. This is the first quantitative analysis of the consistency of the infor- 5 mation provided by these two instruments on surface emissions of CO in an inverse Inversion of TES and modeling context. We focus on observations of CO for November 2004, when the cli- MOPITT data matological emission inventory in the GEOS-Chem model significantly underestimated the atmospheric abundance of CO as observed by TES and MOPITT. We find that D. B. A. Jones et al. both datasets suggest significantly greater emissions of CO from sub-equatorial Africa 10 and the Indonesian/Australian region. The a posteriori emissions from sub-equatorial Africa based on TES and MOPITT data were 173 Tg CO/yr and 184 Tg CO/yr, respec- Title Page

tively, compared to the a priori of 95 Tg CO/yr. In the Indonesian/Australian region, Abstract Introduction the a posteriori emissions inferred from TES and MOPITT data were 155 Tg CO/yr and 185 Tg CO/yr, respectively, whereas the a priori was 69 Tg CO/yr. The differences Conclusions References 15 between the a posteriori emission estimates obtained from the two datasets are gen- Tables Figures erally less than 20%, and are likely due to the different spatio-temporal sampling of the measurements. The a posteriori emissions significantly improve the simulated distribution of CO, however, large regional residuals remain, reflecting systematic errors J I

in the analysis. For example, the a posteriori emissions obtained from both datasets J I 20 do not completely reduce the underestimate in the model of CO column abundances over the southern tropical Atlantic, southern Africa, and over the Indian Ocean, where Back Close

biases of 3–7% remain. Over eastern Asia the a posteriori emissions overestimate the Full Screen / Esc CO column abundances by about 3–6%. These residuals reﬂect the sensitivity of the top-down source estimates to systematic errors in the analysis. Our results indicate Printer-friendly Version 25 that improving the accuracy of top-down emission estimates will require further char- acterization of model biases (chemical and transport) and the use of spatial-temporal Interactive Discussion inversion resolutions consistent with the information content of the observations. EGU

17626 1 Introduction ACPD Inverse modeling has emerged as a powerful tool for estimating surface emissions of environmentally important trace gases. In this context, inverse modeling of observa- 7, 17625–17662, 2007 tions of atmospheric CO has become a widely used tool to improve “bottom-up” es- 5 timates of surface emissions of CO, which are highly uncertain. Atmospheric CO is Inversion of TES and a product of incomplete combustion as well as a byproduct of the oxidation of atmo- MOPITT data spheric hydrocarbons. Accurate and precise estimates of emissions of CO are important since atmospheric CO plays a critical role in determining the oxidative capacity of D. B. A. Jones et al. the atmosphere as the primary sink of OH, and it is also a useful tracer of atmospheric 10 transport. As a combustion product, atmospheric CO provides a useful proxy for combustion- Title Page

related emissions of other precursors of tropospheric O3. It is in this context that Abstract Introduction we conduct an inverse modeling analysis, using the GEOS-Chem chemical transport model (CTM), to quantify emissions of CO for fall 2004. We focus on November 2004 Conclusions References 15 when observations from the Tropospheric Emission Spectrometer (TES) and the Mea- Tables Figures surement of Pollution in The Troposphere (MOPITT) satellite instruments revealed sig- niﬁcantly higher abundances of atmospheric CO in the southern tropics than simulated by the GEOS-Chem model. In a companion paper by Bowman et al. (2007)1 a detailed J I

analysis is presented of the impact on tropical tropospheric O3 of the changes in emis- J I 20 sions of CO (and the implied changes in emissions of other O3 precursors) inferred from the inversion analysis conducted here. Back Close

The dominant source of variability in atmospheric CO in the tropics is associated Full Screen / Esc with biomass burning. Accurately quantifying emissions of CO from these sources is

1Bowman, K. W., Jones, D. B. A., Logan, J. A., Worden, H., Boersma, F., Kulawik, S., Chang, Printer-friendly Version R., Osterman, G., and Worden, J.: Impact of surface emissions to the zonal variability of tropical tropospheric ozone and carbon monoxide for November 2004, Atmos. Chem. Phys. Dis- Interactive Discussion cuss., submitted, available from: http://www.atmosp.physics.utoronto.ca/∼jones/publications. html, 2007. EGU

17627 particularly challenging as they exhibit significant spatial and temporal variability and are sensitive to variations in the climate system such as the El Nino˜ Southern Oscil- ACPD lation (ENSO) (Duncan et al., 2003; Van der Werf et al., 2004). For example, van der 7, 17625–17662, 2007 Werf et al. (2006) found that the standard deviation of the variability in the bottom-up 5 estimates for carbon emissions biomass burning in Indonesia for 1997–2004 was 1.3 times as large as the mean emissions for the period. In late 2004 there was a weak Inversion of TES and El Nino˜ in the tropical Pacific, which could account for some of the discrepancy be- MOPITT data tween the model and observations. In our analysis we focus on quantifying the total combustion-related source of CO from different continental regions, with the assump- D. B. A. Jones et al. 10 tions that biomass burning is the dominant source of direct emissions of CO in the tropics and that interannual variability in biomass burning contributes largely to the dis- Title Page crepancy between the observations and the modeled CO distribution obtained with the climatological emission inventory. Abstract Introduction In the past decade several inverse modelling studies of CO have been conducted Conclusions References 15 using surface, aircraft, and satellite measurements (e.g. Bergamaschi et al., 2000a, b;

Kasibhatla et al., 2002; Petron´ et al., 2002, 2004; Palmer et al., 2003; Heald et al., Tables Figures 2004; Arellano et al., 2004, 2006; Muller¨ and Stavrakou, 2005; Stavrakou and Muller,¨ 2006; Kopacz et al., 2007). These studies have all produced different “top-down” es- J I timates of the regional sources of CO (see discussion in Duncan et al., 2007), reflect- 20 ing differences in inversion frameworks, atmospheric models (e.g. Arellano and Hess, J I 2006), and differences in the datasets employed in the analyses. A particular challenge Back Close for the earlier inversion analyses, which employed surface or aircraft measurements, was the limited spatial and temporal coverage provided by the observations. The re- Full Screen / Esc cent satellite measurements of atmospheric CO, on the other hand, offer significantly 25 greater spatio-temporal coverage and therefore provide more reliable constraints on re- Printer-friendly Version gional surface emissions of CO (Heald et al., 2004). The MOPITT instrument, launched in December 1999, provided the first continuous global measurements of atmospheric Interactive Discussion CO. More recently, space-based measurements of CO have become available from the TES instrument, launched in July 2004. A second objective of this study is to evaluate EGU

17628 the consistency of the top-down constraints on surface emissions of CO provided by observations from these two satellite instruments. We also examine the impact on the ACPD source estimates of differences in the spatio-temporal sampling of the instruments. 7, 17625–17662, 2007 The broad range of published top-down estimates of regional CO emissions reflects 5 the fact that Bayesian inverse modeling is sensitive to systematic errors in the inverse models. Another objective of this study is to demonstrate that, despite the significantly Inversion of TES and increased spatio-temporal coverage offered by data from TES and MOPITT, the CO MOPITT data source estimates inferred from these datasets are sensitive to the presence of systematic errors, such as those associated with the aggregation of the emission regions D. B. A. Jones et al. 10 or the neglect of the non-linearity of the atmospheric chemistry of CO. Assuming that the chemistry of CO is linear is a potential source of systematic error in most inverse Title Page modeling analyses of atmospheric CO, which is not well addressed in the literature. Inversion studies typically prescribe abundances of atmospheric OH to account for the Abstract Introduction chemical sink of CO. However, if large changes in emissions of CO, such as those Conclusions References 15 associated with enhanced biomass burning, are required in an inverse model to ac-

commodate observations of CO, then there should be a concomitant increase in the Tables Figures model of emissions of O3 precursors such as nitrogen oxides (NOx), methane (CH4), and non-methane hydrocarbons (NMHC), which will, in turn, perturb the atmospheric J I abundance of OH. We examine here the potential feedback of perturbations in the 20 atmospheric abundance of OH associated with changes in biomass burning on the J I atmospheric abundance of CO. Back Close In Sect. 2 we begin with a brief description of the CO proﬁle retrievals from the TES and MOPITT instruments. We describe the GEOS-Chem model in Sect. 3. The Full Screen / Esc inversion methodology is presented in Sect. 4, followed by a discussion of the inversion 25 results in Sect. 5. In Sect. 6 we provide a summary of the results and a discussion of Printer-friendly Version their implications for future inversion analyses of atmospheric CO. Interactive Discussion

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17629 2 The MOPITT and TES Instruments ACPD 2.1 MOPITT 7, 17625–17662, 2007 The MOPITT instrument (Drummond and Mand, 1996) was launched on 18 December 1999 on NASA’s Terra spacecraft in a sun-synchronous polar orbit at an altitude of Inversion of TES and 5 705 km with an equator crossing time of 10:30 a.m. local time. It is gas correlation MOPITT data radiometer that measures thermal emission in the 4.7 µm region of the spectrum and has a spatial resolution of 22 km×22 km. The MOPITT observation strategy consists D. B. A. Jones et al. of a 612 km cross-track scan that provides high data density; the instrument achieves nearly complete global coverage every 3 days. 10 Vertical profiles of CO are retrieved from the radiance measurements using an opti- Title Page mal estimation approach, described by Deeter et al. (2003). The retrieved mixing ratios Abstract Introduction are reported on 7 altitude levels, from the surface to 150 hPa. The data have been val- idated by inter-comparison with aircraft and other in-situ measurements (Emmons et Conclusions References al., 2004, 2007). In our analysis we employ version 3 MOPITT data and use profiles Tables Figures 15 between 700–250 hPa, as these are the levels with most of the information in the retrievals (Emmons et al., 2007). The retrieved profiles can be expressed as a linear estimate of the true atmospheric state J I

MOP MOP MOP true MOP J I xˆ = xa + A (x − xa ) + ε (1) MOP true Back Close where xa is the MOPITT a priori CO proﬁle, x is the true atmospheric state vector, MOP 20 A is the MOPITT averaging kernel matrix, and ε is the measurement error. The Full Screen / Esc ∂xˆ MOP averaging kernel is given by A= ∂xtrue and represents the sensitivity of the MOPITT retrieval to the true state of the atmosphere and provides a measure of the vertical Printer-friendly Version resolution of the retrieval. Interactive Discussion

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17630 2.2 TES ACPD The TES instrument is a high resolution Fourier transform spectrometer that was launched on the Aura spacecraft on 15 July 2004. It measures thermal emission be- 7, 17625–17662, 2007 tween 3.3–15.4 µm in both nadir and limb modes (Beer et al., 2001). The satellite is ◦ 5 in a sun-synchronous orbit at an altitude of 705 km with an inclination of 98.2 and a Inversion of TES and repeat cycle of 16 days. The instrument operates in two observational modes: a global MOPITT data survey mode in which the observations are spaced about 5◦ along the orbit track, and a step-and-stare mode in which the observation are made every 40 km long the orbit. D. B. A. Jones et al. The horizontal resolution of the nadir observations is 8 km×5 km. Similar to MOPITT, 10 vertical proﬁles of CO are retrieved from radiance measurements in the 4.7 µm spec- tral region using an optimal estimation approach. The TES retrievals, however, are Title Page

performed for the logarithm of the mixing ratio of CO. A detailed discussion of the TES Abstract Introduction retrievals is given in Bowman et al. (2006). As for MOPITT, the TES retrievals can be expressed as a linear representation of the true atmospheric state Conclusions References TES TES TES true TES 15 xˆ = xa + A (x − xa ) + ε (2) Tables Figures TES TES where xa is the TES a priori CO profile, A is the TES averaging kernel matrix, and ε is the measurement error. For consistency with the inversion using MOPITT data, we J I restrict the TES profiles ingested in the inversion to between 800–250 hPa. J I We employ 6 global surveys of TES CO data that were obtained during 4–16 Novem- 20 ber 2004. There were a limited number of global survey data available before and after Back Close this period in fall 2004. We use version V001 of the TES data as the major change in Full Screen / Esc V002 was associated with improvements in the calibration algorithms to reduce biases in the TES radiances, which decreased the bias in the TES O retrievals in the upper 3 Printer-friendly Version troposphere (Worden et al., 2007). The most significant change in the TES CO product 25 occurred as a result of the warm-up of the optical bench in the instrument in Decem- Interactive Discussion ber 2005 to correct for decreasing signal strength in the 1A1 filter. Before warm-up the sensitivity of the TES CO retrievals had dropped to less than 1 degree of freedom EGU for signal (DOFS) compared to a sensitivity of 1–2 DOFS after launch and after the 17631 warm-up. The DOFS is the trace of the averaging kernel matrix and provides a measure of the number of independent pieces of information on the vertical distribution of ACPD CO available in the retrieved profile. It is, in part, because of the low DOFS in the CO 7, 17625–17662, 2007 retrievals in fall of 2005 that we restrict our analysis to fall 2004. 5 A detailed comparison of the TES and MOPITT profile retrievals of CO was presented by Luo et al., (2007a), while validation of the TES CO retrievals with aircraft ob- Inversion of TES and servations was conducted by Luo et al. (2007b). Luo et al. (2007a) found that despite MOPITT data the differences in the measurement and retrieval techniques of TES and MOPITT, both datasets provide about 0.5–2 degrees of freedom for signal (DOFs) in the troposphere. D. B. A. Jones et al. 10 Luo et al. (2007a) found that after accounting for the a priori profiles incorporated in the retrievals and differences in the averaging kernels between the instruments the two Title Page datasets were in good agreement, with an absolute mean difference of less than 5% in the column abundances of CO. In the vertical profiles, the largest mean differences Abstract Introduction between the two datasets were −4.8% at 150 hPa (for TES compared to MOPITT), Conclusions References 15 whereas the smallest mean differences were −0.2% at 850 hPa (Luo et al., 2007a).

The focus of the work presented here is to assess if this agreement between the two Tables Figures datasets imply consistency in the constraints that they provide on surface emissions of CO when the data are incorporated in an inverse model. J I

J I 3 Inversion methodology Back Close 20 The inversion framework employed here is described in Jones et al. (2003). We obtain optimized estimates of the sources of CO by minimizing the cost function (Rodgers, Full Screen / Esc 2000) Printer-friendly Version T −1 T −1 J(y) = (xˆ − F (y)) Sε (xˆ − F (y)) + (y − ya) Sa (y − ya) (3) Interactive Discussion where xˆ is the observation vector that consists of the retrieved vertical profiles of CO 25 (defined in Eqs. 1 and 2) for MOPITT and TES, respectively), y is the state vector EGU with elements representing monthly mean emissions from the source regions shown 17632 in Fig. 1, and ya is the a priori state vector, which is based on the emission inventory described in Sect. 4 and is given in Table 1. Note, for consistency with the description ACPD of the satellite retrievals in Eqs. (1) and (2), we denote the state vector in the source 7, 17625–17662, 2007 inversion as y although the standard optimal estimation nomenclature as described in 5 Rodgers (2000) uses x as the state vector. Sε is the error covariance matrix of the observation vector and Sa is the a priori covariance matrix. The forward model F (y) Inversion of TES and reflects the transport of the CO emissions in the GEOS-Chem model, and accounts MOPITT data for the vertical sensitivity of TES and MOPITT and the a priori CO profile used in the retrievals in the two datasets. It is given by D. B. A. Jones et al.

MOP MOP MOP MOP 10 F (y) = xa + A (H(y) − xa ) (4) Title Page and Abstract Introduction TES TES TES TES F (y) = xa + A (ln[H(y)] − xa ) (5) Conclusions References where H(y) is the GEOS-Chem model which transports and chemically transforms the CO emissions (y). Tables Figures 15 We assume Gaussian error statistics for the observation and a priori errors, which yields the following maximum a posteriori (MAP) solution for the minimization of the J I cost function (Rodgers, 2000), J I T −1 −1 −1 T −1 −1 yi+1 = yi + (K Sε Ki + Sa ) [K Sε (xˆ − F (yi )) − Sa (yi − ya)]. (6) i i Back Close Here y and K =∂F (y )/∂y are estimates of the state vector and the Jacobian matrix, i i i Full Screen / Esc 20 respectively, at the ith iteration. It is necessary to use an iterative approach in solving for the MAP solution because of the slight nonlinearity introduced by the logarithm in the TES forward model (Eq. 5). We obtain the solution in Eq. (6) using a Gauss-Newton Printer-friendly Version method (Rodgers, 2000), with the sequential approach outlined in Jones et al. (2003). Interactive Discussion The Jacobian is estimated using separate atmospheric tracers of CO for emissions 25 from each region in the state vector, as described in Sect. 4. In each region we solve EGU for the total emissions of CO. 17633 In constructing the a priori error covariance matrix we assume that emissions from the diﬀerent sources are uncorrelated and have a uniform uncertainty of 50%, follow- ACPD ing Palmer et al. (2003), except for emissions from North American and Europe for 7, 17625–17662, 2007 which Palmer et al. (2003) assumed an uncertainty of 30%. We specify a uniform a 5 priori constraint to more clearly assess the impact of the two datasets on the source estimates. Inversion of TES and The observation error covariance matrix consists of the retrieval error covariance, MOPITT data the model error covariance, and the representativeness error covariance. Previous inverse modeling studies (Palmer et al., 2003; Jones et al., 2003; and Heald et al., 2004) D. B. A. Jones et al. 10 for atmospheric CO have shown that the model error dominates the observation error covariance, with the representativeness and measurement errors providing a small Title Page contribution to the total error. In their inversion analysis of Asian emissions of CO, Heald et al. (2004) examined the sensitivity of the estimates of the CO sources to the Abstract Introduction speciﬁcation of the model error. They compared the impact on the source estimates Conclusions References 15 of specifying a uniform model error with that obtained using the complete model error

covariance structure for the GEOS-Chem simulation of CO, as determined by Jones et Tables Figures al. (2003). They found that there was sufficient information in the MOPITT data such that the source estimates for regions with strong biomass burning emissions of CO, J I such as southeast Asia, were insensitive to the specification of the error covariance 20 structure. Assuming a uniform estimate of 20% for the total observation error in their J I analysis, for example, did not significantly influence the estimates for source regions Back Close such as southeast Asia. In contrast, they found that the weaker regional sources, such as estimates for emissions from western China and Japan and Korea were influenced Full Screen / Esc strongly by the choice of error covariance. As our focus here is on the dominant, 25 continental-scale, biomass burning signals in the tropics, we assume a uniform obser- Printer-friendly Version vation error of 20%. This approach is similar to previous inverse modeling studies of CO such as Kasibhatla et al. (2002) and Petron´ et al. (2002). We also neglect the cor- Interactive Discussion relations in the model error, but account for the influence of the averaging kernels from the instruments on the model error, which captures the vertical correlations associated EGU

17634 with the vertical smoothing of the retrievals. ACPD 4 The GEOS-Chem model 7, 17625–17662, 2007

The GEOS-Chem model (Bey et al., 2001) is a global three-dimensional CTM driven Inversion of TES and by assimilated meteorological observations from the NASA Goddard Earth Observing MOPITT data 5 System (GEOS-4) from the Global Modeling and data Assimilation Office (GMAO). The ◦ ◦ meteorological fields have a horizontal resolution of 1 ×1.25 with 55 levels in the verti- D. B. A. Jones et al. cal, and a temporal resolution of 6 h (3 h for surface fields). We employ version 7-02-04 of GEOS-Chem (http://www-as.harvard.edu/chemistry/trop/geos), with the resolution ◦ ◦ of the meteorological fields degraded to 2 latitude × 2.5 longitude. Recent applica- Title Page 10 tions of the model have been described in a range of studies (e.g. Suntharalingam et al., 2004; Heald et al., 2004; Park et al., 2005; Hudman et al., 2007;and Wang Abstract Introduction

et al., 2007). The model includes a complete description of tropospheric O3-NOx- Conclusions References hydrocarbon chemistry, including the radiative and heterogeneous effects of aerosols. The GEOS-Chem simulation of CO for 4–15 November 2004 is shown in Fig. 2. The Tables Figures 15 model significantly underestimates the CO abundances as observed by TES and MO- PITT, reflecting the climatological emission inventories used in the simulation. Anthro- J I pogenic emissions of CO in this version of GEOS-Chem are as described by Duncan et al. (2007), with emissions for the base year of 1985 in the inventory scaled to 1998. For J I biomass burning we employ the emission inventory of Duncan et al. (2003), whereas Back Close 20 for biofuel combustion we use estimates from Yevich and Logan (2003). The total a priori global emissions of CO, as well as the total secondary source of CO from the Full Screen / Esc oxidation of methane and NMHC is given in Table 1. In our inversion analysis we specify monthly mean concentrations of OH to linearize Printer-friendly Version the chemistry of CO. This approach has been used previously for the inverse modeling Interactive Discussion 25 of CO using the GEOS-Chem model (e.g. Palmer et al., 2003, 2006; Jones et al., 2003; Heald et al., 2004; and Kopacz et al., 2007). Linearization of the chemistry enables us to efficiently calculate the Jacobian in the inversion analysis by specifying a separate EGU 17635 tracer for each region considered in the state vector. These OH fields were obtained from an earlier version of the full chemistry simulation of GEOS-Chem (Evans and ACPD Jacob, 2005). 7, 17625–17662, 2007

5 Results Inversion of TES and MOPITT data 5 The results of the inversion analysis using data from TES and MOPITT for early Novem- ber 2004 are shown in Fig. 3 and Table 1. They suggest signiﬁcantly greater emissions D. B. A. Jones et al. of CO in sub-equatorial Africa and the Indonesian/Australian region than the a priori. In sub-equatorial Africa, where the a priori estimate is 95 Tg CO/yr (Table 1), the inferred emission estimate from TES data is 173 Tg CO/yr, while the estimate based Title Page 10 on MOPITT data is 184 Tg CO/yr. In the Indonesian/Australian region the a posteriori emission estimates are 155 Tg CO/yr and 185 Tg CO/yr, based on the TES and MO- Abstract Introduction

PITT data, respectively, compared to the a priori of 69 Tg CO/yr. Following Palmer et Conclusions References al. (2003) and Heald et al. (2004) we assume here that the seasonality of the sources in the model is correct and, therefore, report the source estimates as annual means al- Tables Figures 15 though the inversion provides constraints on the source estimates only for the October to November period, reflecting the atmospheric lifetime of CO. In the analysis we do not J I attempt to discriminate between emissions of CO from different source types, such as biomass burning or fuel combustion, however, we assume that the increases in emis- J I sions suggested for the tropical regions are associated mainly with greater emissions Back Close 20 of CO from biomass burning. The globally averaged source of CO estimated from the TES and MOPITT measurements are 2646 Tg CO/yr and 2761 Tg CO/yr, respectively, Full Screen / Esc compared to the a priori of 2243 Tg CO/yr (Table 1). The agreement between the global estimates from TES and MOPITT is encouraging, but it should be noted that the pre- Printer-friendly Version scribed OH fields have a global mean OH abundance of 11.3×105 cm−3, and biases in Interactive Discussion 25 the chemistry, as reflected in the OH abundances, would adversely impact the accuracy of the source estimates from both instruments, despite their apparent consistency. The a posteriori estimates of the CO emissions for sub-equatorial Africa and the EGU 17636 Indonesian/Australian region based on the TES data thus represent an increase in emissions by a factor of 1.8 and 2.3, respectively, above the a priori values. The ACPD large increase in emissions from these regions are consistent with the results of Arel- 7, 17625–17662, 2007 lano et al. (2006), who conducted an inversion analysis of MOPITT data for 2000– 5 2001 and reported similarly greater top-down estimates of CO emissions. Arellano et al. (2006) estimated total a posteriori emissions from Indonesia and Oceania (which Inversion of TES and includes Australia) of about 165 Tg CO/yr, which is comparable to the estimates of MOPITT data 155–185 Tg CO/yr obtained in this study. For sub-equatorial Africa they reported an estimate of 203 Tg CO/yr, slightly larger than the 175–185 Tg CO/yr obtained here. It D. B. A. Jones et al. 10 should be noted that in their analysis, Arellano et al. (2006) quantified separately CO emissions from fuel combustion and biomass burning and found that fuel combus- Title Page tion provided a significant contribution to the total a posteriori emission estimates for Indonesia and sub-equatorial Africa. For example, their a posteriori estimate for emis- Abstract Introduction sions from fuel combustion from Indonesia and Oceania were 84 Tg Co/yr and about Conclusions References 15 5 Tg CO/yr, respectively. In contrast, their a posteriori estimate for biomass burning

from these regions was 76 Tg CO/yr. We do not believe that our inversion approach Tables Figures can reliably discriminate between emissions of CO from fuel combustion and biomass burning. However, the consistency between our results for 2004 and those of Arellano J I et al. (2006) for 2000 does suggest that fuel combustion may indeed be responsible for 20 a large fraction of the emissions as these sources have less interannual variability. J I In a companion paper by Bowman et al. (2007)1 the a posteriori emissions from the Back Close inversion are evaluated in the context of their impact on the modeled O3 distribution. Using a forward model simulation of GEOS-Chem with the O3 precursor emissions Full Screen / Esc from fuel combustion and biomass burning scaled by the regional scaling factors ob- 25 tained in the CO inversion, Bowman et al. (2007) showed that the a posteriori emis- Printer-friendly Version sions provide an improved simulation of O3 over Indonesia and Australia. Throughout Interactive Discussion the free troposphere over Indonesia and Australia O3 increased in the model by as much as 10 ppb, reducing the maximum bias in the modeled O3 distribution relative to TES by about 40%. In contrast, in the free troposphere over the tropical southern EGU

17637 1 Atlantic, Bowman et al. (2007) found that the improvement in the modeled O3 with the a posteriori emissions was modest, despite the large increases in surface emissions ACPD from sub-equatorial Africa, reflecting the greater influence of NOx from lightning on the 7, 17625–17662, 2007 budget of O3 in this region. 5 The results obtained here show that TES and MOPITT data provide consistent constraints on surface emissions of atmospheric CO. As shown in Table 1, the absolute Inversion of TES and differences in the source estimates inferred from the two datasets are about 20% or MOPITT data less, with the exception of emissions from North America, which is discussed further D. B. A. Jones et al. below. These differences represent the potential influence on the source estimates of 10 the different spatio-temporal sampling of the TES and MOPITT measurements when

the data are incorporated into a regional Bayesian inverse analysis. It is likely that a Title Page higher resolution inverse model than that used in this study, which optimizes the emissions at the spatio-temporal resolution of the data, may result in smaller diﬀerences Abstract Introduction between the source estimates inferred from the two datasets. Conclusions References 15 We conducted a sensitivity analysis using observations from MOPITT for the whole month of November (5–28) to determine the potential impact on the source estimates of Tables Figures using only two week of data on early November. We found that the MOPITT data for 5–

28 November data produced results similar to those obtained with data for the ﬁrst half J I of November, suggesting that the information from early November is representative 20 of the sources of CO for the October-November period. This is consistent with the J I

simulation experiments of Jones et al. (2003) that suggested two weeks of TES would Back Close provide sufficient information to quantify large-scale continental sources of CO. The inversion analysis can independently quantify emissions from the three conti- Full Screen / Esc nental regions in the southern tropics because transport patterns from these regions 25 are broad and are relatively distinct, reflecting the local meteorology. The distribution Printer-friendly Version of the tagged CO tracers emitted from South America, subequatorial Africa, and the Indonesian/Australian region are shown in Fig. 4. Emissions from South America are Interactive Discussion entrained into the subtropics in the southern tropical Atlantic and are transported eastward in the westerlies in the subtropics and extratropics of the southern hemisphere. EGU 17638 Emissions from sub-equatorial Africa, on the other hand, are exported across the In- dian Ocean and Australia. Over Australia and Indonesia the dominant contribution to ACPD the total abundance of CO in the free troposphere is from local emissions convec- 7, 17625–17662, 2007 tively transported out of the boundary layer. In general, over each continental region, 5 convective transport of surface emissions to the upper troposphere and subsequent eastward outflow provides a strong signal for CO in the middle troposphere where TES Inversion of TES and and MOPITT are most sensitive. MOPITT data While the focus on this analysis is on the tropics, the inverse model is global in scope and the major discrepancy between the results obtained with TES data and D. B. A. Jones et al. 10 those from MOPITT data is in the estimate for North American emissions. The TES data suggest a significantly reduced North American source of CO compared to the Title Page a priori, whereas the MOPITT data suggest a slightly larger source (Table 1). This reduction of the estimate of the North American source with the TES data reflects the Abstract Introduction fact that during boreal fall North American emissions provide a small contribution to Conclusions References 15 the total CO abundance in the free troposphere at midlatitudes. As shown in Fig. 5,

North American emissions account for less than about 16% of the total CO in the middle Tables Figures troposphere at midlatitudes, which means that the signal for North American emissions is more challenging to discriminate from the background, given the noise level in the J I inversion analysis. We found that with TES data the North American source estimate 20 is correlated with the European estimate (r=−0.6) and with the background source J I of CO (r=−0.5). In contrast, with MOPITT data the North American and European Back Close source estimates are uncorrelated (r=−0.09), while the North American estimate is correlated with the background (r=−0.6). This suggests that the estimate for the North Full Screen / Esc American source obtained with both datasets for this period will be sensitive to errors 25 in the background source. Printer-friendly Version The inversion is less sensitive to the North American source because these emissions are widely distributed at high latitudes in the Arctic region, but in our inversion Interactive Discussion analysis we neglect retrievals poleward of 60◦ from both satellites as they tend to be less reliable. At midlatitudes there are episodic transport events, such as warm EGU

17639 conveyor belts associated with the passage of cold fronts, which transport air out of the North American boundary layer and produce enhanced levels of CO in the free ACPD troposphere. These signals, however, are localized spatially and temporally and are 7, 17625–17662, 2007 therefore not captured by the TES data. MOPITT, on the other hand, because of its 5 greater spatio-temporal sampling density can resolve these synoptic structures (Liu et al., 2006) and therefore provide more constraints on North American emissions. Inversion of TES and The discrepancy between the North American estimates illustrates the importance MOPITT data of properly selecting the state vector in the inversion analysis that is consistent with the spatio-temporal sampling of the observation used in the analysis. Ideally, the estimates D. B. A. Jones et al. 10 for those elements of the state vector for which the observations provide little information should remain at the values speciﬁed by the a priori. The low estimate of the North Title Page American source obtained with the TES data indicate the presence of systematic errors in the inverse model. Abstract Introduction The results obtained using both TES and MOPITT data suggest signiﬁcantly larger Conclusions References 15 emissions from Asia, 511 Tg CO/yr and 531 Tg CO/yr, respectively, compared to the a

priori estimate of 367 Tg O/yr. In contrast, the inverse modeling studies of Heald et Tables Figures al. (2004) and Arellano et al. (2006), which inferred CO emissions from MOPITT data, reported estimates for the Asian emissions of CO (excluding emissions from Indone- J I sia) of 282 Tg CO/yr and 402 TgCO/yr, respectively. The differences between the a 20 posteriori estimates of the Asian sources reported by these studies are due, in part, J I to the fact that these studies used different model configurations. Furthermore, the Back Close analyses were focused on different periods: our analysis was carried out for November 2004, whereas the Heald et al. (2004) study was focused on February–April 2001, and Full Screen / Esc Arellano et al. (2006) conducted a time dependent inversion analysis for April 2000– 25 April 2001. These studies will, therefore, be impacted differently by the spatio-temporal Printer-friendly Version sampling of the data, aggregation of the source regions, and by systematic errors associated with the transport and chemistry. It is also possible that some fraction of Interactive Discussion the differences between the Asian estimates reported here and those from the earlier studies could reflect actual increases in emissions of CO in Asia since 2000. However, EGU

17640 examination of the residuals from the CO simulation with the a posteriori emissions (discussed below), show that the Asian estimates reported here do provide an over- ACPD estimate of the Asian sources of CO, and are likely due to systematic errors in the 7, 17625–17662, 2007 inversion. 5 As mentioned above, the source estimates obtained from two weeks of MOPITT data are consistent with those inferred from data for the whole month of November 2004. Inversion of TES and The exception is for north equatorial Africa, for which the inversion using the latter MOPITT data dataset suggests a much larger estimate for the CO emissions compared to results with the former. Biomass burning, which is the dominant source of CO from north equatorial D. B. A. Jones et al. 10 Africa, increases during boreal fall and winter, reaching a maximum in December– January (Duncan et al., 2003). Indeed, ﬁre-counts inferred from the MODIS instrument Title Page show more widespread burning in late November 2004, compared to early November. This increased burning later in the month is reﬂected in the larger source estimate Abstract Introduction obtained when MOPITT data from late November is included in the inversion analysis. Conclusions References

15 5.1 Comparison of GEOS-Chem with a posteriori emissions to TES and MOPITT Tables Figures The a posteriori emissions provide a significantly improved simulation of the distribution of CO, as shown in Table 2 and Figs. 6 and 7. The global mean bias (averaged J I 60◦ S–60◦ N) in the modeled column abundances of CO with respect to the TES data J I is reduced from −12% to 0.1%, while the bias with respect to the MOPITT data is re- 20 duced from −22% to −0.8%. Despite the significant improvement in the global mean Back Close CO, the model simulation with the optimized emissions produces large regional biases, Full Screen / Esc as shown in Figs. 6, 7, and Table 2. The residuals of the model fit to the TES and MO- PITT data shown in Figs. 6b and 7b and Table 2 indicate that the inversion with both datasets provides an underestimate of 3–7% of the CO column abundances over the Printer-friendly Version 25 southern tropical Atlantic, southern Africa, and over the Indian Ocean. Interactive Discussion The optimized emissions inferred from MOPITT data produce an underestimate of CO abundances over the Sahara, the Middle East, and over the North Pacific, while EGU they result in an overestimate of CO abundances across the tropical Pacific and over 17641 tropical western Africa. These regional biases, however, are compensatory such that the mean bias across the tropics and midlatitudes of the southern hemisphere (0◦– ACPD 45◦ S) is small, −3% and −1% for emissions inferred from TES and MOPITT data, 7, 17625–17662, 2007 respectively. Similarly, in the extratropics of the northern hemisphere (25◦–60◦ N) the 5 mean biases in the model, relative to the TES and MOPITT data, are reduced from −7% and −18%, respectively, to 0.9% and −2.7% with the a posteriori emissions. Inversion of TES and The fact that the reduced North American emissions obtained with the TES data do MOPITT data not contribute to a noticeable bias between the model simulation with the a posteriori emissions and the TES observations over North America is an indication that, as dis- D. B. A. Jones et al. 10 cussed above, North American emissions provide a small contribution to the total CO in the free troposphere in boreal fall. Title Page The large a posteriori estimate for Asian emissions represents an overestimate of the Asian sources, as reflected in the large negative residuals over East Asia (Figs. 6b Abstract Introduction and 7b and Table 2). As mentioned above, this is likely due to a combination of ag- Conclusions References 15 gregation errors in the inversion analysis and bias in the model transport or chemical

fields. With the a priori emissions, the model underestimates the CO abundance over Tables Figures the north Pacific compared to the observations from TES and MOPITT. Emissions of CO from southeast Asia represent a large contribution to the total CO in the upper tro- J I posphere over the Pacific. The a priori bias over the North Pacific could reflect either an 20 underestimate in the magnitude of these emissions or a bias in the rate at which these J I emissions are transported to the upper troposphere (mostly likely by convective trans- Back Close port). By aggregating all of the Asian emissions into one region, the inversion analysis scales all of the Asian emissions in trying to compensate for this underestimate of CO Full Screen / Esc over the North pacific, potentially resulting in an overestimate of the eastern Asian 25 emissions. As demonstrated recently by Kopacz et al. (2007), conducting the inversion Printer-friendly Version at the resolution of the model would provide maximum flexibility in adjusting the emissions to best reproduce the observations (given the uncertainty of the observations Interactive Discussion and the model simulation), while minimizing the aggregation errors. EGU

17642 5.2 Feedback of changes in atmospheric OH on CO ACPD In the inversion analysis we linearized the chemistry of CO by imposing monthly mean concentrations of OH, the main sink for atmospheric CO. This, however, introduces bi- 7, 17625–17662, 2007 ases in the inversion as observations of CO ingested in the analysis will also reflect the 5 influence of OH concentrations characteristic of different background chemical condi- Inversion of TES and tions in the atmosphere. For example, enhanced combustion-related emissions of CO MOPITT data in Indonesia/Australia, as suggested by the observations, will result in increased emis- D. B. A. Jones et al. sions of O3 precursors, leading to elevated atmospheric abundances of O3 and OH, and consequently to suppressed concentrations of CO. To assess the magnitude of 10 this feedback on the atmospheric concentrations of CO we conducted a forward model simulation with the full nonlinear chemistry and with the combustion-related emissions Title Page

of NOx (from biomass burning and biofuel and fossil fuel combustion) scaled uniformly Abstract Introduction according to the regional scaling factors obtained in the inversion analysis of the CO data. We scale only NOx emissions in the simulation and compare the resulting CO Conclusions References 15 distribution with that obtained with the a priori NOx emissions. We neglect possible Tables Figures errors associated with assuming that the relative contribution of diﬀerent source types to the total emissions of NOx is the same as for emissions of CO in these regions. The change in the abundance of CO obtained with the scaled NOx emissions is J I

shown in Fig. 8. The increased emissions of NOx from Indonesia and Australia results J I 20 in a reduction of CO by as much as 7–10 ppb, corresponding to about 10% of the total Back Close CO abundance. This decrease in CO is a result of increased tropospheric O3, and thus OH, in the Indonesian/Australian region. Higher concentrations of NOx produce an in- Full Screen / Esc crease in O3 throughout the southern tropics, with the largest increases, of about 35%, in the middle/upper troposphere over Indonesian/Australian (not shown). A decrease Printer-friendly Version 25 of about 7–10 ppb in CO over Indonesia and Australia represents about 20–30% of the contribution of emissions from this region to the total abundance of CO (Fig. 5) and Interactive Discussion suggests that neglecting the chemical feedback of changes in surface emissions on the abundance of OH, and thus CO, could introduce biases in the a posteriori estimates of EGU

17643 the sources of CO. Indeed, in their inversion analysis of surface measurements of CO and GOME observations of NO2,Muller¨ and Stavrakou (2005) found that their a pos- ACPD teriori CO emissions obtained by simultaneously optimizing the CO and NOx sources 7, 17625–17662, 2007 provided a better simulation of aircraft observations of CO than those estimated from 5 only the surface CO data. Inversion of TES and MOPITT data 6 Summary and discussion D. B. A. Jones et al. We have conducted an inverse modeling analysis of observations of atmospheric CO from the TES and MOPITT satellite instruments to assess the constraints that these data provide on estimates of surface emissions of CO. We focused our analysis on Title Page 10 observations from November 2004, during the biomass burning season in the southern hemisphere, as emissions of CO from biomass burning represent the dominant source Abstract Introduction

of variability in atmospheric CO in the tropics. Observations from 4–15 November Conclusions References 2004, indicated that the climatological emissions inventory in the GEOS-Chem model significantly underestimated the abundance of CO observed by both instruments. We Tables Figures 15 used a maximum a posteriori inverse modeling approach to quantify the magnitude of emissions of CO most consistent with the observations. Although our focus was on J I the tropics, the inversion analysis was global, employing profile retrievals of CO from MOPITT and TES between 60◦ S–60◦ N. J I The TES and MOPITT data provided consistent information on the CO sources; dif- Back Close 20 ferences between the a posteriori emission estimates obtained from the two datasets were generally less than 20%. We found that both datasets suggested significantly Full Screen / Esc greater emissions of CO (by a factor of 2–3) from sub-equatorial Africa and from the Indonesian/Australian region in November 2004. The a posteriori emissions Printer-friendly Version from sub-equatorial Africa based on TES and MOPITT data were 173 Tg CO/yr and Interactive Discussion 25 184 Tg CO/yr, respectively, compared to the a priori of 95 Tg CO/yr. In the Indone- sian/Australian region, a posteriori emissions inferred from TES and MOPITT data were 155 Tg CO/yr and 185 Tg CO/yr, respectively, whereas the a priori was 69 Tg CO/yr. In EGU 17644 contrast, the a posteriori emissions from South America were not significantly different from the a priori. ACPD We found that while the source estimates for the TES and MOPITT were consistent, 7, 17625–17662, 2007 the inversion using both datasets was sensitive to the presence of systematic errors in 5 the analysis. The inversion produced much larger estimates for emissions from Asia, 511 Tg CO/yr based on TES data and 531 Tg CO/yr based on MOPITT data, which Inversion of TES and are greater than most previously published estimates of Asian emissions. These a MOPITT data posteriori emissions result in large residuals over East Asia, with the model simulation providing an overestimate of the observed CO from both TES and MOPITT. In contrast, D. B. A. Jones et al. 10 the model simulation with the a priori emissions underestimated the observed CO over this region. The residual bias over East Asia is likely a result of aggregation errors and Title Page biases in the model transport. The contribution of aggregation error to the residual bias could be minimized by constraining the Asian emissions at a higher spatial resolution Abstract Introduction thereby allowing the inverse model to adjust the Asian emissions locally rather than Conclusions References 15 uniformly scaling all of the emissions across Asia. Ideally, the inversion should be

conducted at the resolution of the model using an adjoint modeling approach such as Tables Figures that employed by Stavrakou and Muller¨ (2006) and Kopacz et al. (2007). We also examined the feedback on atmospheric CO of variations in tropospheric OH J I associated with changes in biomass burning emissions, as suggested by the a poste- 20 riori CO source estimates. Using a forward model simulation of the full tropospheric J I chemistry with NOx emissions from combustion sources scaled uniformly based on Back Close the regional scaling factors inferred in the CO inversion analysis produced increases in O3 and OH throughout the southern tropics with the largest increases over Indone- Full Screen / Esc sia/Australia. The abundance of O3 increased by about 35% in the middle/upper tropo- 1 25 sphere over Indonesian/Australia. Bowman et al. (2007) show that the regional O3 re- Printer-friendly Version sponse to enhanced combustion-related emissions is more complex over regions such as South America and sub-equatorial Africa when emissions of hydrocarbons such as Interactive Discussion acetaldehyde, acetone, and formaldehyde are considered in addition to emissions of NOx. In response to the changes in O3 and OH, the modeled CO abundance with the EGU 17645 scaled NOx emissions decreased by about 10% over Indonesia/Australia, for example, compared to the simulation with the a priori NOx emissions. This reduction in CO rep- ACPD resented about 20–30% of the contribution of emissions from the Indonesian/Australian 7, 17625–17662, 2007 region to the total abundance of CO and suggests that neglecting the influence of NOx 5 emissions (and of the emission of other precursors of O3) on the CO chemistry could contribute to a significant bias in the CO source estimates. To accurately quantify the Inversion of TES and surface emissions of CO in an inverse modeling framework, it will be necessary to MOPITT data properly account for the chemical coupling of the CO-O3-NOx-hydrocarbon chemistry. We found that although both TES and MOPITT provide global observational cov- D. B. A. Jones et al. 10 erage, differences in the spatio-temporal sampling of the measurements contribute to differences between the a posteriori source estimates obtained with the two datasets. Title Page As discussed above, these differences were small (less than 20%) for most of the continental source regions considered here, but they are nonetheless significant. For North Abstract Introduction American emissions, to which there was less sensitivity in the inversion, the difference Conclusions References 15 between the estimates from TES and MOPITT was about 75%, reflecting the influence

of systematic errors in the analysis. It is likely that some of the discrepancies between Tables Figures previously published top-down emissions estimates could be due to the use of ad hoc state vectors based on geo-political boundaries, which are independent of the spatio- J I temporal resolution and precision of the data used in the analyses. Furthermore, the 20 presence of systematic errors in the analysis will adversely impact the elements of the J I state vectors which are poorly constrained by the observations. We showed that al- Back Close though the a posteriori source estimates provided a signiﬁcantly improved simulation of the TES and MOPITT data, regional residual biases remained in the simulated CO Full Screen / Esc distribution, which are probably due to aggregation errors in the inversion state vector 25 and to systematic models errors. The presence of these residuals, despite the con- Printer-friendly Version sistency between the source estimates inferred from TES and MOPITT, suggests that reconciling the discrepancies between top-down source estimates will require properly Interactive Discussion characterizing systematic observation and model errors (chemical and transport) and conducting the inversion at a spatial-temporal resolution consistent with the information EGU

17646 content of the observations. It also indicates the need to optimally combine boundary layer measurements of CO with TES and MOPITT data, along with observations of ACPD other tracers such as NO , which have similar sources as CO, in a multi-species inver- 2 7, 17625–17662, 2007 sion framework.

5 Acknowledgements. This work was supported by funding from the Natural Sciences and En- Inversion of TES and gineering Research Council of Canada. J. A. Logan was funded by grants from NASA. The MOPITT data GEOS-Chem model is managed at Harvard University with support from the NASA Atmo- spheric Chemistry Modeling and Analysis Program. D. B. A. Jones et al.

References Title Page

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G., Fu, Q., Vay, 7, 17625–17662, 2007 5 S., and Sachse, G. W.: Using CO2:CO correlations to improve inverse analyses of carbon fluxes, J. Geophys. Res., 111, D12318, doi:10.1029/2005JD006697, 2006. Palmer, P. I., Jacob, D. J., Jones, D. B. A., Heald, C. L., Yantosca, R. M., Logan, J. A., Inversion of TES and Sachse, G. W., and Streets, D. G.: Inverting for emissions of carbon monoxide from MOPITT data Asia using aircraft observations over the western Pacific, J. Geophys. Res., 108, 8825, D. B. A. Jones et al. 10 doi:10.1029/2002JD003176, 2003. Petron,´ G., Granier, C., Khattatov, B., Lamarque, J.-F., Yudin, V., Muller, J.-F., and Gille, J. C.: Inverse modeling of carbon monoxide surface emissions using CMDL network observations, J. Geophys. Res., 107, 4761, doi:10.1029/2001JD002049, 2002. Title Page Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, World Sci- 15 entific, Singapore, 2000. Abstract Introduction Stavrakou, T. and Muller,¨ J.-F.: Grid-based versus big region approach for inverting CO emissions using Measurement of Pollution in the Troposphere (MOPITT) data, J. Geophys. Res., Conclusions References 111, D15304, doi:10.1029/2005JD006896. Tables Figures Suntharalingam, P., Jacob, D. J., Palmer, P. I., Logan, J. A., Yantosca, R. M., Xiao, Y., Evans, 20 M. J., Streets, D., Vay, S. A., and Sachse, G.: Improved quantification of Chinese carbon fluxes using CO2/CO correlations in Asian outflow, J. Geophys. Res., 109, D18S18, J I doi:10.1029/2003JD004362, 2004. van der Werf, G. R., Randerson, J. T., Collatz, G. J., Giglio, L., Kasibhatla, P. S., Arellano, A. J I F., Olsen, S. C., and Kasischke, E. S.: Continental-scale partitioning of fire emissions during Back Close 25 the 1997 to 2001 El Nino/La Nina period, Science, 303, 5654, 73–76, 2004. van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., and Arellano, Full Screen / Esc A. F.: Interannual variability in global biomass burning emissions from 1997 to 2004, Atmos. Chem. Phys., 6, 3423–3441, 2006, Printer-friendly Version http://www.atmos-chem-phys.net/6/3423/2006/. 30 Wang, Y. X., McElroy, M. B., Martin, R. V., Streets, D. G., Zhang, Q., and Fu, T.-M.: Interactive Discussion Seasonal variability of NOx emissions over east China constrained by satellite observations: Implications for combustion and microbial sources, J. Geophys. Res., 112, D06301, doi:10.1029/2006JD007538, 2007. EGU 17650 Worden, H. M., Logan, J., Worden, J. R., Beer, R., Bowman, K., Clough, S. A., Eldering, A., Fisher, B., Gunson, M. R., Herman, R. L., Kulawik, S. S., Lampel, M. C., Luo, M., Megret- ACPD skaia, I. A., Osterman, G. B., and Shephard, M. W.: Comparisons of Tropospheric Emission Spectrometer (TES) ozone profiles to ozonesodes: methods and initial results, J. Geophys. 7, 17625–17662, 2007 5 Res., 112, D03309, doi:10.1029/2006JD007258, 2007. Yevich, R. and Logan, J. A.: An assesment of biofuel use and burning of agricultural waste in the developing world, Global Biogeochem. Cy., 17(4), 1095, doi:10.1029/2002GB001952, Inversion of TES and 2003. MOPITT data

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Table 1. A priori and a posteriori source estimates. Inversion of TES and MOPITT data A priori1 (Tg CO/yr) A posteriori A posteriori A posteriori Diﬀerence Region TES MOPITT MOPITT between D. B. A. Jones et al. Nov 4–16 Nov 5–15 Nov 5–28 estimates (Tg CO/yr) (Tg CO/yr) (Tg CO/yr) from TES and MOPITT3 (%) BB BF FF Total Title Page North America 23 6 106 135 36 146 165 −75 Europe 8 17 85 110 132 111 111 19 Abstract Introduction Asia 102 93 171 367 511 531 483 −4 South America 69 19 25 113 118 141 157 −16 Northern Africa 99 21 19 139 131 119 174 10 Conclusions References Sub-equatorial 78 10 6 95 173 184 185 −6 Africa Tables Figures Indonesia/ 49 7 13 69 155 185 165 −16 Australia Rest of the World2 1215 1390 1344 1336 3 Total 2243 2646 2761 2776 −4 J I

J I 1 Sources represent emissions of CO from fossil fuel (FF) and biofuel combustion (BF) and biomass burning (BB), based on Duncan et al. (2003, 2007). Back Close 2 The rest of the world source includes CO from the oxidation of methane and non-methane hydrocarbons. Full Screen / Esc 3 Diﬀerence in TES a posteriori estimates relative to those from MOPITT calculated from data in early November. Printer-friendly Version

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17652 ACPD 7, 17625–17662, 2007 Table 2. Regional bias in the model simulation of CO1. Inversion of TES and TES MOPITT MOPITT data Region A priori A posteriori A priori A posteriori D. B. A. Jones et al. bias (%) bias (%) bias (%) bias (%) Globe −12 0.1 −22 −0.8 ◦ ◦ (60 S–0 N) Title Page Southern hemisphere −18 −2 −27 −1 ◦ ◦ (0 –5 S) Abstract Introduction Southern Atlantic −18 −3 −27 −5 (35◦–0◦ S, 40◦ W–5◦ E) Conclusions References Southern Africa/Indian Ocean −20 −5 −31 −7 (35◦–5◦ S, 15◦–0◦ E) Tables Figures Central Paciﬁc Ocean −14 0.3 −13 8 ◦ ◦ ◦ ◦ (10 S–10 N, 180 W–0 W) J I Northern hemisphere −7 1 −18 −3 ◦ ◦ (25 N–60 N) J I North Paciﬁc −10 −0.9 −24 −8 (25◦–0◦ N, 175◦ W–120◦ W) Back Close East Asia −4 3 −12 6 Full Screen / Esc (25◦–60◦ N, 110◦ E–135◦ E)

Printer-friendly Version 1 The bias is calculated (model minus observations) with respect to CO columns from TES and MOPITT data, with the a priori and a posteriori emissions of CO in the model. Interactive Discussion

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Full Screen / Esc Fig. 1. Source regions comprising the state vector in the inversion analysis. The a priori estimates for emissions from these regions are listed in Table 1. Printer-friendly Version

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Fig. 2a, b. (a) Column abundances of CO (in units molecules cm−2) from TES, averaged 4–15 Printer-friendly Version

November 2005. (b) Column abundances of CO from the GEOS-Chem model. The modeled

ﬁelds were sampled along the TES orbit and smoothed using the TES averaging kernels and a Interactive Discussion priori proﬁle. White areas are regions without observations. EGU

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−2 Fig. 2c, d. (c) Column abundances of CO (in units molecules cm ) from MOPITT, averaged Printer-friendly Version 5–15 November 2004. (d) Column abundances of CO from the GEOS-Chem model. The modeled ﬁelds were sampled along the MOPITT orbit and smoothed using the MOPITT averaging Interactive Discussion kernels and a priori proﬁle. White areas are regions without observations.

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J I Fig. 3. A proiri and a posteriori CO source estimates. Black bars indicate the a priori emis- Back Close sion estimates, red bars are the a posteriori estimates inferred from TES data (4–15 November 2004), whereas the blue bars are the a posteriori estimates based on MOPITT data (5–15 Full Screen / Esc November 2004). The green bars represent the a posteriori emission estimates obtained using MOPITT data for 5–28 November 2004. The regional deﬁnitions are: North America (NAm), Europe (EU), South America (SAm), North Africa (NAf), sub-equatorial Africa (SAf), Indone- Printer-friendly Version sia/Australia (Indo Aus), and the rest of the world and the background chemical source of CO Interactive Discussion (ROW). Error bars indicate an uncertainty of 1-σ.

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Fig. 4. Distribution of the tagged CO tracer for emissions of (a) South America, (b) sub- Printer-friendly Version equatorial Africa, and (c) Indonesia/Australia. The tracer distributions were obtained using the a priori CO emissions and are shown for the upper troposphere at 8 km, averaged between Interactive Discussion 1–15 November 2004. Units are in ppb. EGU

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Fig. 5. Contribution of emissions of CO from North America (as deﬁned in Fig. 1) to the total Back Close abundance of CO. The tracer distribution, as a percentage of total CO, is shown for 00:00 GMT on 5 November 2005 at about 5 km. Full Screen / Esc

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Fig. 6. (a) Column abundances of CO, averaged from 4–15 November 2004, from the GEOS- Printer-friendly Version Chem simulation of the a posteriori emissions. Modeled fields transformed using the TES Interactive Discussion averaging kernels and a priori profiles. Units are 1018 molecules cm−2. (b) The residuals expressed as a percent difference between the model and the TES observations (model minus TES). The TES data are shown in Fig. 2. EGU

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Printer-friendly Version Fig. 7. (a) Column abundances of CO, averaged from 5–15 November 2004, from the GEOS-

Chem simulation of the a posteriori emissions. Modeled fields transformed using the MOPITT Interactive Discussion 18 −2 averaging kernels and a priori profiles. Units are 10 molecules cm . (b) The residuals expressed as a percent difference between the model and the MOPITT observations (model minus MOPITT). The MOPITT data are shown in Fig. 2. EGU 17661

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J I Fig. 8. Diﬀerence in modeled CO at 8 km in the GEOS-Chem model between simulations with Back Close the a priori emissions and with combustion related NOx emissions scaled based on the regional scaling factors from the CO inversion. Shown are the a priori minus scaled NOx simulations Full Screen / Esc averaged between 1–15 November 2004. Units are in ppb. Printer-friendly Version

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